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Agile Data Science 2.0: Building FullStack Data Analytics Applications With Spark,Used
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Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if theyre to succeed. With the revised second edition of this handson guide, upandcoming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikitlearn, and Apache Airflow. Youll learn an iterative approach that lets you quickly change the kind of analysis youre doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the datavalue pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track
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